Bibliographic record
Abstract
A absentee workers 9 access access to care homes 19 access to facilities 116-17, 119 access to social life 122 see also universal access to care admission to a care home 18, 76-7 acceptance deadlines 20, 25, 28 admission process 12, 97, 130 after the move 25-7 delayed admission 20 first encounter with family 90-1 managing at home beforehand 21-2, 27 navigation and advocacy 19, 23-5 in rural areas 102-3 waiting lists 19, 20, 24, 28, 63 advocacy 23, 28-9, 76-7 aging in place 18-19, 28, 123 aging population 5, 8 Andersson, Jonas 121, 122 Anna's shower 38-41 anonymity 101, 108 apartments see rooms and facilities Armstrong, Pat 2, 30, 62 art/ artistic events 52, 54, 120 assessment tools 37-8 assisted living see supportive housing Australian study 87 autonomy 33, 34, 35, 36, 44 B Banerjee, A. 30 Barken, R. 73 barriers around care homes 123 bathing/ washing 3, 90, 128 bathing at home 23 grooming 26, 34 showering 33, 38-41 teeth cleaning 90, 93 bathrooms 11, 12, 87, 116, 118 Bauer, M. 96, 97 beds, number of 11-12 in Canada 7, 19-20, 63, 87, 102 in Norway 6, 8, 87, 102, 124 in Sweden 6, 8, 18, 27, 102 bingo 57, 81 birthdays 88 body work 13, 33-8, 43-4 and disabilities 34 and service providers 34 boundaries 66 between care and self-care 43-4
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.863 | 0.783 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".